import numpy as np
import pandas as pd 
import geopandas as gpd
import os 
from matplotlib import pyplot as plt

Polygon

data_path ='/home/tandonsa/PycharmProjects/github_sam991/Leveraging-GPS-Data-for-Machine-Learning/Data/4+-+Data+Vizualization/data'
small_areas = gpd.read_file(os.path.join(data_path,'malmo-pop.shp'))
small_areas.head()

	fid	Deso	Age_5	Age6_9	Age10_15	Age16_19	Age20_24	Age25_29	Age30_34	Age35_39	...	Age45_49	Age50_54	Age55_59	Age60_64	Age65_69	Age70_74	Age75_79	Age80_w	Total	geometry
0	2822.0	1280C1590	125.0	82.0	113.0	83.0	80.0	104.0	99.0	109.0	...	99.0	115.0	88.0	93.0	101.0	112.0	141.0	410.0	2057.0	POLYGON ((373380.287 6160661.365, 373382.480 6...
1	2823.0	1280C1430	332.0	201.0	262.0	141.0	165.0	217.0	212.0	195.0	...	140.0	139.0	140.0	101.0	75.0	54.0	39.0	35.0	2616.0	POLYGON ((372667.079 6159991.971, 372653.152 6...
2	2824.0	1280C1220	128.0	69.0	65.0	34.0	93.0	132.0	135.0	109.0	...	71.0	91.0	91.0	119.0	153.0	110.0	92.0	122.0	1696.0	POLYGON ((372812.733 6158498.565, 372817.194 6...
3	2825.0	1280C1490	76.0	70.0	118.0	92.0	102.0	57.0	48.0	89.0	...	108.0	101.0	94.0	80.0	59.0	62.0	35.0	62.0	1340.0	POLYGON ((374168.622 6160181.233, 374169.100 6...
4	2826.0	1280C1530	220.0	94.0	118.0	74.0	150.0	200.0	196.0	163.0	...	113.0	81.0	107.0	92.0	77.0	45.0	31.0	32.0	1928.0	POLYGON ((375219.802 6160462.505, 375274.357 6...

5 rows × 21 columns

small_areas.crs

<Projected CRS: EPSG:3006>
Name: SWEREF99 TM
Axis Info [cartesian]:
- N[north]: Northing (metre)
- E[east]: Easting (metre)
Area of Use:
- name: Sweden - onshore and offshore.
- bounds: (10.03, 54.96, 24.17, 69.07)
Coordinate Operation:
- name: SWEREF99 TM
- method: Transverse Mercator
Datum: SWEREF99
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich

print(small_areas.shape)
small_areas.plot(figsize=(10,9),color='blue',alpha=0.5,edgecolor="#CFAC84")

(202, 21)

<matplotlib.axes._subplots.AxesSubplot at 0x7fed24c4add8>

Lines

roads = gpd.read_file(os.path.join(data_path,"malmo-streets.shp"))
roads.head()

	osm_id	name	highway	waterway	aerialway	barrier	man_made	z_order	other_tags	geometry
0	23440666	Blekingsborgsgatan	residential	None	None	None	None	3	None	LINESTRING (374183.208 6161017.321, 374184.590...
1	23618297	ÄrtholmsvÃĪgen	residential	None	None	None	None	3	None	LINESTRING (373860.815 6160562.585, 373773.040...
2	28552693	Kransgatan	residential	None	None	None	None	3	None	LINESTRING (373648.221 6160291.937, 373678.893...
3	28552694	TÃķrngatan	residential	None	None	None	None	3	None	LINESTRING (373520.164 6160333.786, 373548.277...
4	28552695	SÃķdertorpsvÃĪgen	residential	None	None	None	None	3	None	LINESTRING (373340.961 6160304.145, 373370.603...

roads.crs

<Projected CRS: EPSG:3006>
Name: SWEREF99 TM
Axis Info [cartesian]:
- N[north]: Northing (metre)
- E[east]: Easting (metre)
Area of Use:
- name: Sweden - onshore and offshore.
- bounds: (10.03, 54.96, 24.17, 69.07)
Coordinate Operation:
- name: SWEREF99 TM
- method: Transverse Mercator
Datum: SWEREF99
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich

print(roads.shape)
roads.plot(figsize=(5,7),edgecolor="#CFAC84",alpha=0.8)

(24551, 10)

<matplotlib.axes._subplots.AxesSubplot at 0x7fed24a852e8>

Points

preschools = gpd.read_file(os.path.join(data_path,'preschools.shp'))
preschools.head()

	BESÖKSADR	BESÖKSORT	FIRMABEN	FÖRETAGSN	KOM	KOMTEXT	POSTADRE00	POSTADRESS	POSTNUMMER	geometry
0	VIKINGAGATAN 60	MALMÖ	FÖRSKOLA	MALMÖ MONTESSORISKOL	1280	Malmö	VIKINGAGATAN 60	MALMÖ	21774	POINT (371044.000 6162502.000)
1	KAMRERGATAN 11	MALMÖ	None	JUDISKA FÖRSKOLAN CH	1280	Malmö	KAMRERGATAN 11	MALMÖ	21156	POINT (374769.000 6163245.000)
2	VANÅSGATAN 66	MALMÖ	None	RUMÄNSKA ORTODOXA FÖ	1280	Malmö	VANÅSGATAN 66	MALMÖ	21620	POINT (371788.000 6161201.000)
3	GRÖNVÄGEN 6	ARLÖV	FÖRSKOLA GRÖNEBO	BURLÖVS KOMMUN	1231	Burlöv	BOX 53	ARLÖV	23221	POINT (379012.000 6166772.000)
4	KORNVÄGEN 1	ARLÖV	FÖRSKOLA SVENSHÖG	BURLÖVS KOMMUN	1231	Burlöv	BOX 53	ARLÖV	23221	POINT (379450.000 6167390.000)

preschools.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7fed24a53c50>

Multi layer

fig,ax = plt.subplots(figsize=(9,11))
small_areas.plot(ax=ax,color="#F1ECEB",alpha=0.7)
preschools.plot(ax=ax,marker='+',color='black',edgecolor='white',alpha=0.8)
roads.plot(ax=ax,color='grey',alpha=0.1)
plt.title("Malmo Neighbourhoods",fontsize=40,color='grey')
plt.show()

Choropleth Maps

Visualize density

small_areas.columns

Index(['fid', 'Deso', 'Age_5', 'Age6_9', 'Age10_15', 'Age16_19', 'Age20_24',
       'Age25_29', 'Age30_34', 'Age35_39', 'Age40_44', 'Age45_49', 'Age50_54',
       'Age55_59', 'Age60_64', 'Age65_69', 'Age70_74', 'Age75_79', 'Age80_w',
       'Total', 'geometry'],
      dtype='object')

small_areas.plot(column='Age80_w',figsize=(12,10)) # choropleth maps 

<matplotlib.axes._subplots.AxesSubplot at 0x7fed19095b70>

small_areas.plot(column='Age80_w',figsize=(12,10) ,cmap='Blues',scheme='quantiles',k=6,legend=True) # choropleth maps  

<matplotlib.axes._subplots.AxesSubplot at 0x7fecb04f1278>

Normalize the Population

The right way to show th crololeth maps is to normalize the data first

small_areas['Age80_perc']=(small_areas['Age80_w']/small_areas["Total"])*100
small_areas["Age80_perc"]

0      19.931940
1       1.337920
2       7.193396
3       4.626866
4       1.659751
         ...    
197     1.081359
198     6.577225
199     0.809353
200     8.840864
201     2.761104
Name: Age80_perc, Length: 202, dtype: float64

fig,ax = plt.subplots(figsize=(12,10))
small_areas.plot(column='Age80_perc',cmap='Blues',scheme='quantiles',k=6,legend=True,ax=ax)

<matplotlib.axes._subplots.AxesSubplot at 0x7fecb036d2b0>

fig,ax = plt.subplots(1,2,figsize=(20,18))
small_areas.plot(column='Age80_perc',cmap='Blues',scheme='quantiles',k=6,ax=ax[0],legend=True)
ax[0].set_title("Normalized Choropleth Map")

small_areas.plot(column='Age80_w',cmap='Blues',scheme='quantiles',k=6,ax=ax[1],legend=True)
ax[0].set_title("Raw data Choropleth Map")

Text(0.5, 1.0, 'Raw data Choropleth Map')

Bubble points

Simply give column name in the marker size

small_areas_points = small_areas.copy()
small_areas_points['geometry'] = small_areas_points['geometry'].centroid
small_areas_points.head()

	fid	Deso	Age_5	Age6_9	Age10_15	Age16_19	Age20_24	Age25_29	Age30_34	Age35_39	...	Age50_54	Age55_59	Age60_64	Age65_69	Age70_74	Age75_79	Age80_w	Total	geometry	Age80_perc
0	2822.0	1280C1590	125.0	82.0	113.0	83.0	80.0	104.0	99.0	109.0	...	115.0	88.0	93.0	101.0	112.0	141.0	410.0	2057.0	POINT (373736.130 6160844.324)	19.931940
1	2823.0	1280C1430	332.0	201.0	262.0	141.0	165.0	217.0	212.0	195.0	...	139.0	140.0	101.0	75.0	54.0	39.0	35.0	2616.0	POINT (372932.457 6160165.211)	1.337920
2	2824.0	1280C1220	128.0	69.0	65.0	34.0	93.0	132.0	135.0	109.0	...	91.0	91.0	119.0	153.0	110.0	92.0	122.0	1696.0	POINT (373183.207 6158866.154)	7.193396
3	2825.0	1280C1490	76.0	70.0	118.0	92.0	102.0	57.0	48.0	89.0	...	101.0	94.0	80.0	59.0	62.0	35.0	62.0	1340.0	POINT (374515.204 6160556.319)	4.626866
4	2826.0	1280C1530	220.0	94.0	118.0	74.0	150.0	200.0	196.0	163.0	...	81.0	107.0	92.0	77.0	45.0	31.0	32.0	1928.0	POINT (375533.769 6160531.143)	1.659751

5 rows × 22 columns

small_areas_points.plot(color='grey')

<matplotlib.axes._subplots.AxesSubplot at 0x7fecb011c8d0>

# to plot bubble plot , we need to add column name in marker size
fig,ax = plt.subplots(figsize=(8,10))
small_areas_points.plot(ax=ax,color='grey',markersize="Age80_w")
plt.axis('equal')
plt.show()

# to plot bubble plot , we need to add column name in marker size
fig,ax = plt.subplots(figsize=(8,10))
small_areas.plot(ax=ax,color="lightgrey",alpha=0.7,edgecolor='grey',linewidth=0.4)
small_areas_points.plot(ax=ax,color="#07424A",markersize="Age80_w",categorical=False,legend=True)
plt.axis('equal')
plt.show()

Folium Visualization -Part1

from matplotlib import pyplot as plt 
import geopandas as gpd
import folium
import os

countries = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
data_path = '/home/tandonsa/PycharmProjects/github_sam991/Leveraging-GPS-Data-for-Machine-Learning/Data/5-+Interactive+Visualization/data'

# conflicts_dataset points 
confilct_path = os.path.join(data_path,'acled2019.shp')
acled = gpd.read_file(confilct_path)

acled.head()

	data_id	iso	event_id_c	event_id_n	event_date	year	time_preci	event_type	sub_event_	actor1	...	latitude	longitude	geo_precis	source	source_sca	notes	fatalities	timestamp	iso3	geometry
0	6768128	887	YEM45982	45982	31 December 2019	2019	1	Battles	Armed clash	Military Forces of Yemen (2012-)	...	14.3541	47.0765	2	Yemen Data Project; Aden al Ghad; Al Janoob al...	Local partner-National	On 31 December 2019, clashes reportedly erupte...	0	1580761030	YEM	POINT (47.07650 14.35410)
1	6768129	887	YEM45983	45983	31 December 2019	2019	1	Strategic developments	Change to group/activity	Military Forces of Yemen (2012-) Security Belt...	...	14.3541	47.0765	2	Al Janoob al Youm; Yemen Data Project	Local partner-National	On 31 December 2019, forces loyal to the South...	0	1580761030	YEM	POINT (47.07650 14.35410)
2	6768130	887	YEM45984	45984	31 December 2019	2019	1	Battles	Armed clash	Military Forces of Yemen (2012-)	...	14.2081	47.1570	1	Al Janoob al Youm; Aden al Ghad; Yemen Data Pr...	Local partner-National	On 31 December 2019, clashes reportedly erupte...	1	1580761030	YEM	POINT (47.15700 14.20810)
3	6715395	356	IND68948	68948	31 December 2019	2019	1	Protests	Peaceful protest	Protesters (India)	...	20.2724	85.8338	1	Pioneer (India)	National	On 31 December, students' wing of INC and Mili...	0	1578514389	IND	POINT (85.83380 20.27240)
4	6943236	222	SLV861	861	31 December 2019	2019	1	Violence against civilians	Attack	B-18: Barrio-18	...	13.5894	-89.2383	1	El Salvador.com	National	On 31th December 2019 in Huizucar (La Libertad...	1	1582839958	SLV	POINT (-89.23830 13.58940)

5 rows × 32 columns

print("Latitude: ",acled.latitude.min())
print("Longitude:",acled.longitude.max())

Latitude:  -54.9244
Longitude: 158.6541

m = folium.Map(location=[53.431085,-2.960422])
m

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# Zoom
m = folium.Map(location=[53.431085,-2.960422],zoom_start=15)
m

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# change base map 
m = folium.Map(location=[53.431085,-2.960422],zoom_start=15,tiles="CartoDB dark_matter")
m 

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folium.Marker(location=[53.431085,-2.960422],
             popup='<i>Stanley Park</i>'
             ).add_to(m)
m

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folium.Circle(location=[53.431085,-2.960422],
              radius=25,
              popup='Anfield Stadium',
              color='crimson',
              fill=True
             ).add_to(m)
m

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Acled Visualization

# direct using Geojaons
m = folium.Map([4.546647,22.373178],zoom_start=2,tiles='cartodbpositron')

folium.GeoJson(acled.sample(1000)).add_to(m)
m

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m = folium.Map([4.546647,22.373178],zoom_start=2,tiles='cartodbpositron')
acled_sample = acled.sample(1000)
locs_points = zip(acled_sample.geometry.y,acled_sample.geometry.x)

for location in locs_points:
    folium.CircleMarker(location=location , color='red',radius=0.5).add_to(m)
m

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from folium.plugins import FastMarkerCluster
cmap = folium.Map([4.546647,22.373178],zoom_start=2,tiles='cartodbpositron')

FastMarkerCluster(data=list(zip(acled_sample.geometry.y.values,acled_sample.geometry.x.values))).add_to(cmap)
folium.LayerControl().add_to(cmap)
cmap

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Interactive maps with folium -Part2

from matplotlib import pyplot as plt 
import geopandas as gpd
import numpy as np
import pandas as pd
import folium
import os

countries = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

data_path = '/home/tandonsa/PycharmProjects/github_sam991/Leveraging-GPS-Data-for-Machine-Learning/Data/5-+Interactive+Visualization/data'

# conflicts_dataset points 
confilct_path = os.path.join(data_path,'acled2019.shp')

acled = gpd.read_file(confilct_path,mask=countries[countries["continent"]=='Africa'])

print(acled.shape)
acled.head()

(23641, 32)

	data_id	iso	event_id_c	event_id_n	event_date	year	time_preci	event_type	sub_event_	actor1	...	latitude	longitude	geo_precis	source	source_sca	notes	fatalities	timestamp	iso3	geometry
0	6714129	706	SOM30213	30213	31 December 2019	2019	1	Battles	Armed clash	Al Shabaab	...	2.3963	45.0159	1	Undisclosed Source	Local partner-Other	31 December 2019. In Warmahan, Al Shabaab mili...	0	1578512393	SOM	POINT (45.01590 2.39630)
1	6723349	12	ALG7951	7951	31 December 2019	2019	1	Protests	Peaceful protest	Protesters (Algeria)	...	36.7525	3.0420	1	Liberte (Algeria); TSA Algerie; El Watan (Alge...	National	On 31 December 2019, students held their 45th ...	0	1579554007	DZA	POINT (3.04200 36.75250)
2	6723350	12	ALG7953	7953	31 December 2019	2019	1	Protests	Peaceful protest	Protesters (Algeria)	...	36.3650	6.6147	1	Liberte (Algeria); El Watan (Algeria)	National	On 31 December 2019, students held their 45th ...	0	1579554007	DZA	POINT (6.61470 36.36500)
3	6713368	404	KEN7182	7182	31 December 2019	2019	1	Violence against civilians	Attack	Police Forces of Kenya (2013-)	...	-3.3543	40.0209	1	Daily Nation (Kenya)	National	On December 31 2019, a suspected Al Shabaab re...	1	1578512391	KEN	POINT (40.02090 -3.35430)
4	6718756	180	DRC16324	16324	31 December 2019	2019	1	Battles	Armed clash	Military Forces of Democratic Republic of Cong...	...	-1.1869	29.4461	2	Kivu Security Tracker	Local partner-Other	On 31 December 2019, the FARDC attacked Mayi M...	0	1578943802	COD	POINT (29.44610 -1.18690)

5 rows × 32 columns

HeatMap

from folium.plugins import HeatMap, HeatMapWithTime
df = acled[['latitude','longitude']]
df.head()

	latitude	longitude
0	2.3963	45.0159
1	36.7525	3.0420
2	36.3650	6.6147
3	-3.3543	40.0209
4	-1.1869	29.4461

print("Latitude MAX {}, MIN {}".format(df['latitude'].max(),df['latitude'].min()))
print("Longitude MAX {}, MIN {}".format(df['longitude'].max(),df['longitude'].min()))

Latitude MAX 37.2744, MIN -34.4311
Longitude MAX 50.8255, MIN -17.4441

heat_data= list(df.values)
heat_data[:10]

[array([ 2.3963, 45.0159]),
 array([36.7525,  3.042 ]),
 array([36.365 ,  6.6147]),
 array([-3.3543, 40.0209]),
 array([-1.1869, 29.4461]),
 array([34.9346, -2.7332]),
 array([12.9894,  7.6006]),
 array([-3.0451, 30.1592]),
 array([32.2463, -8.5294]),
 array([13.3877, 22.4228])]

m = folium.Map([4.546647,22.373178],zoom_start=2,tiles='cartodbpositron')
HeatMap(heat_data).add_to(m)## accepts data in form of list
m

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gradient = {
    '0.0':'blue',
    '1':'red'
}
m = folium.Map([4.546647,22.373178],zoom_start=2,tiles='cartodbpositron')
HeatMap(heat_data,gradient=gradient).add_to(m)
m

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gradient = {
    '0':'Black',
    '0.4':'Purple',
    '0.6':'Red',
    '0.8':'Yellow',
    '1':'White'
}
m = folium.Map([4.546647,22.373178],zoom_start=2,tiles='cartodbpositron')
HeatMap(heat_data,gradient=gradient).add_to(m)
m

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Animation

df_2019  = acled[acled['year']==2019]
df_2019_latlon = df_2019[['latitude','longitude','event_date']]
df_2019_latlon.head()

	latitude	longitude	event_date
0	2.3963	45.0159	31 December 2019
1	36.7525	3.0420	31 December 2019
2	36.3650	6.6147	31 December 2019
3	-3.3543	40.0209	31 December 2019
4	-1.1869	29.4461	31 December 2019

df_2019_latlon['heat'] = pd.to_datetime(df_2019_latlon['event_date'])

/home/tandonsa/anaconda3/envs/pygeo/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.

df_2019_latlon.head()

	latitude	longitude	event_date	heat
0	2.3963	45.0159	31 December 2019	2019-12-31
1	36.7525	3.0420	31 December 2019	2019-12-31
2	36.3650	6.6147	31 December 2019	2019-12-31
3	-3.3543	40.0209	31 December 2019	2019-12-31
4	-1.1869	29.4461	31 December 2019	2019-12-31

df_2019_latlon['month']= df_2019_latlon['heat'].dt.month
df_2019_latlon['date'] = df_2019_latlon['heat'].dt.date
data  = df_2019_latlon[['latitude','longitude','month']]
data.sample(10)

/home/tandonsa/anaconda3/envs/pygeo/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.
/home/tandonsa/anaconda3/envs/pygeo/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  

	latitude	longitude	month
2852	35.5553	6.1786	11
20691	9.2000	12.4833	2
6813	14.4004	33.5184	9
20935	2.2949	45.6357	2
15116	36.0732	4.7611	5
11373	-1.3426	40.7281	7
17034	35.4358	7.1433	4
1212	2.0844	45.3900	12
6069	5.2833	36.7667	9
4044	2.6195	44.8936	10

import seaborn as sns
sns.countplot(data['month'])

/home/tandonsa/anaconda3/envs/pygeo/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning

<AxesSubplot:xlabel='month', ylabel='count'>

data_heat  = [ [ [row['latitude'],row['longitude']] for index, row in data[data['month'] == i].iterrows()] for i in range(1,13)]

m = folium.Map([4.546647,22.373178],zoom_start=2,tiles='cartodbpositron')
HeatMapWithTime(data_heat,auto_play=True,max_opacity=0.8).add_to(m)## accepts data in form of list
m

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IPYleaflet

from ipyleaflet import (Map,GeoData,basemaps,WidgetControl,GeoJSON,
                        LayersControl,Icon,Marker,basemap_to_tiles,
                        Choropleth,MarkerCluster,
                        Heatmap,SearchControl,FullScreenControl)
import os 
import numpy as np
import pandas as pd 
from ipywidgets import Text, HTML # 
from branca.colormap import linear
import geopandas as gpd
import json

data_path = '/home/tandonsa/PycharmProjects/github_sam991/Leveraging-GPS-Data-for-Machine-Learning/Data/5-+Interactive+Visualization/data'

countries = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

asia_acled = gpd.read_file(os.path.join(data_path,'acled2019.shp'),mask=countries[countries['continent']=='Asia'])
asia_acled.head()

	data_id	iso	event_id_c	event_id_n	event_date	year	time_preci	event_type	sub_event_	actor1	...	latitude	longitude	geo_precis	source	source_sca	notes	fatalities	timestamp	iso3	geometry
0	6768128	887	YEM45982	45982	31 December 2019	2019	1	Battles	Armed clash	Military Forces of Yemen (2012-)	...	14.3541	47.0765	2	Yemen Data Project; Aden al Ghad; Al Janoob al...	Local partner-National	On 31 December 2019, clashes reportedly erupte...	0	1580761030	YEM	POINT (47.07650 14.35410)
1	6768129	887	YEM45983	45983	31 December 2019	2019	1	Strategic developments	Change to group/activity	Military Forces of Yemen (2012-) Security Belt...	...	14.3541	47.0765	2	Al Janoob al Youm; Yemen Data Project	Local partner-National	On 31 December 2019, forces loyal to the South...	0	1580761030	YEM	POINT (47.07650 14.35410)
2	6768130	887	YEM45984	45984	31 December 2019	2019	1	Battles	Armed clash	Military Forces of Yemen (2012-)	...	14.2081	47.1570	1	Al Janoob al Youm; Aden al Ghad; Yemen Data Pr...	Local partner-National	On 31 December 2019, clashes reportedly erupte...	1	1580761030	YEM	POINT (47.15700 14.20810)
3	6715395	356	IND68948	68948	31 December 2019	2019	1	Protests	Peaceful protest	Protesters (India)	...	20.2724	85.8338	1	Pioneer (India)	National	On 31 December, students' wing of INC and Mili...	0	1578514389	IND	POINT (85.83380 20.27240)
4	6714375	356	IND68961	68961	31 December 2019	2019	1	Protests	Peaceful protest	Protesters (India)	...	25.2905	79.8753	1	Amar Ujala	Subnational	On Dec 31, protests were held by auto drivers ...	0	1578514386	IND	POINT (79.87530 25.29050)

5 rows × 32 columns

BaseMaps , Marker and Icons

centre = [57.71,11.98]
zoom= 12
m  = Map(basemap = basemaps.Esri.WorldImagery,centre=centre,zoom=zoom)
m

Map(center=[0.0, 0.0], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_t…

stamen = basemap_to_tiles(basemaps.Stamen.Toner)
m.add_layer(stamen)

marker  = Marker(location=centre,draggable=False)
m.add_layer(marker)
m

Map(bottom=65736.0, center=[0.0, 0.0], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_tit…